Multi-Robot Systems

1. Multi-Robot Systems CSCI 7000-006 Wednesday, September 2, 2009 NikolausCorrell

2. So far Reactive algorithms Lowest-level control for individual autonomous robots Coordination mechanism for swarms of individually simple individuals Coordination via local communication or by modifying the environment

3. Today More advanced reactive algorithms Message propagation and information gradients Threshold-based algorithms

4. Information Gradients Example: find hot-spot in the environment Given Robot swarm Every robot can sample temperature Robots can exchange info Course question Algorithm What else do you need to know? 4 4 4 7 8 3 9 7 6 7 6 6 5 5 2 5

5. Tracking down a hot spot Need local range and bearing or global position Algorithm: Sample temperature Broadcast temperature Receive from neighbors Broadcast direction to hottest neighbor Other application: routingYouTube Open E-puck Range & Bearing Miniaturized Board for Local Communication in Swarm Robotics. A. Gutierrez, A. Campo, M. Dorigo, J. Donate, F. Monasterio-Huelinand L. Magdalena. ICRA 2009.

6. Information Gradients: Static and Mobile Systems Similar to artificial potential fields But: robots/embedded systems provide the force field Multiple sources can emit competing gradients http://borg.cc.gatech.edu/gnats

7. Information Gradients: Programming Language MIT Proto (Scheme dialect) Functional language “Program” evaluates into a motion vector Commands Integrate over neighborhood Differentiate over neighborhood (gradient) … Key: every information used in the program is also broadcast (def cluster-to (src) (* -1 (grad (distance-to src)))) (cluster-to (is-light)) http://groups.csail.mit.edu/stpg/proto.html

8. Example: Deployment of Wifi Networks Goal: Communication Infrastructure Self-Deployment, Self-Repair Applications Natural disasters Military Environmental Monitoring

9. Challenges 3m (GPS) Positioning Bearing Unidirectional communication Local-to-global: coding & algorithms Locomotion Its just me here! Heeello! Everybody maintain coverage!

10. Algorithm Only sensor: number of neighbors Move randomly until local topology constraints are fulfilled Connected to gateway Minimum number of neighbors Maximum number of neighbors 3 2 2 4 1 if ( 2 < neighbors < 4) move else stop

11. How to establish connection? Gateway emits special message Nodes forward gateway message “Hop-count” to gateway Count-to-infinity problem Unconnected if number of hops larger than total number of nodes 2 1 0 2 3 0 3 4 0

12. Proto Algorithm Parameters

13. System Architecture 1 Hz IP Layer IP Layer IP Layer IP Layer IP Layer IP Layer IP Layer OLSRd (Optimized Link State Routing) Link Layer Link Layer Link Layer Link Layer Link Layer Link Layer Atheros MIPS 180MHz Mobile Layer Mobile Layer Mobile Layer Mobile Layer Mobile Layer Mobile Layer Link Layer Deployment Algorithm PROTO Mobile Layer iRobot Create 20 Hz Differential Wheels Drive

14. Topology vs. control parameters(centralized deployment) ( 1 < neighbors < 3) ( 2 < neighbors < 4) ( 3 < neighbors < 5)

15. Area coverage vs. control parameters(centralized deployment) ( 2 < neighbors < 4) ( 1 < neighbors < 5) ( 1 < neighbors < 6)

16. Indoor Experiment 9 robots Central deployment 500m2 ~ 30min

17. Taking advantage of Localization and Communication Baseline: deployment algorithm Algorithms Move to last-known-good position Move to last-known-good and disperse Consensus and flocking Course Question: Why would you need the consensus and flocking algorithm? Hint: local minima of the dispersion algorithm. Videos: Anna Derbakova

18. Threshold-based algorithms React probabilistically to stimulus Threshold determines likelihood Different thresholds for competing stimuli Biological inspiration: social insects Probability to do task i Stimulus intensity Krieger, M.J.B. & Billeter, J-B. (2000). The call of duty: Selforganised task allocation in a population of up to twelve mobile robots Robotics Autonom. Sys. 30: 65-84.

19. Example 1: Task Allocation(Krieger & Billeter) Goal: forage for “energy” in the environment Maintain energy in the “nest” on a certain level Ant-like task allocation and recruitment in cooperative robots. M. Krieger, J. Billeter and L. Keller. Nature, vol 406, pp. 992-995, August 2006.

20. System architecture and algorithm Nest maintains information on colony energy and broadcast this information Robots receive energy from the nest Robots forage randomly for “food”, increasing nest energy Robots forage only when nest energy falls below a certain threshold Beacon provides visual cue for finding the nest

21. Results Krieger, M.J.B. & Billeter, J-B. (2000). The call of duty: Selforganised task allocation in a population of up to twelve mobile robots Robotics Autonom. Sys. 30: 65-84.

22. Example 2: Aggregation Goal: Aggregation (Monday) Threshold-based task allocation Workers estimate availability of work (stimulus) Workers take a rest when there is little work Self-regulation of swarm activity

23. Results

24. Analysis Homogenous controllers Local perception of stimulus (heterogeneous team) leads to diversity Fully scalable: number of agents do not matter Course question: What happens if threshold is too low What happens if threshold is too high How find optimal threshold?

25. Optimizing the response threshold Systematic search /parameter sweep Performance metric is steadily increasing Comparing snap-shot of the experiment at T=10h Too few workers Too many workers (clusters get destroyed) 10 robots, 20 seeds

26. Summary Reactive algorithms, information gradients and response-thresholds are powerful heuristics for generating complex behavior Information gradients enabled by range and bearing hardware Potential for extreme miniaturization and large numbers of agents

27. Upcoming Friday: Message passing in ROS Next week: Monday: Labor Day Wednesday: (multi-robot) Localization Friday: Lab, navigation